Abstract

This study aims to evaluate the precipitation bias in the grey zone simulation (~ 15 km) using the Central Weather Bureau Global Forecast System (CWBGFS). We develop a new evaluation method using the object-based precipitation system (OPS) to examine the bias associated with the degree of convection organization. The 2016 South China Sea (SCS) Summer Monsoon onset is selected to evaluate the model’s performance due to its sharp transition of large-scale circulation, which contributes to the complexity of precipitation pattern. The results based on OPS show that the observed precipitation tends to aggregate toward the central part of SCS during the post-onset period, while the precipitation in the model distributes more sparsely over the ocean. The observed precipitation intensity increases with the size of OPS especially for the extremes; however, the model underrepresents the relationship between the precipitation spectrum and the size of OPS. Moreover, the model simulates earlier diurnal peak time of precipitation over land in the organized systems than observation. The results also suggest that the convection scheme is insensitive to column moisture during the pre-onset period which seems to be one of the key factors to the excessive precipitation in the model. Using high horizontal resolution, however, does not improve the simulation of precipitation much in the model. The current study suggests that the precipitation bias related to aggregation of the convective systems should be regarded as an essential objective of model evaluation and improvement.

Notes

Acknowledgements

This study is jointly supported by the Central Weather Bureau in Taiwan (1052281C, 1062221C), and the Ministry of Science and Technology in Taiwan (MOST-106-2111-M-002-005, MOST-106-2111-M-002-008, MOST-106-1502-02-11-01). We acknowledge the Central Weather Bureau providing the computational resources for this work. The IMERG V04 data were provided by the NASA/Goddard Space Flight Center and PPS, which develop and compute the IMERG V04 as a contribution to GPM, and archived at ftp://arthurhou.pps.eosdis.nasa.gov/gpmdata/ YYYY/MM/DD/imerg/ (accessed at 30 March 2017). The NCEP GDAS/FNL0.25 data were provided by the Computational and Information Systems Laboratory at the National Center for Atmospheric Research, and archived at https://rda.ucar.edu/ datasets/ds083.3/ (accessed at 29 October 2017).

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix A

To demonstrate that the precipitation bias persists even with the increase in horizontal resolution, we compare the HR (higher resolution, ~ 15 km, which is used in the main text) and the operational CWBGFS (hereafter lower resolution (LR), ~ 25 km) using four kinds of evaluation score. Table 2 shows the bias fraction, and the root mean square error (RMSE) over the SCS region in the pre- and post-onset periods in the HR and the LR. The bias fraction represents the ratio of temporal mean average precipitation rate over the SCS region (the outer dashed box in Fig. 4a) in the model to the observation, which could represent the precipitation bias in general. In the HR, the bias fraction in the pre-onset period (1.64) is more significant than the post-onset period (1.09), and their differences with the LR are small. The RMSE in the pre-onset period (0.64 mm h−1) is smaller than the post-onset period (1.71 mm h−1), and their differences with the LR are also small. The result shows that the model overestimates the precipitation during the onset, especially the pre-onset period. The RMSEs in the post-onset period are more significant than the pre-onset period, which is supposed to be caused by the underrepresentation of location and the intensity of the precipitation hotspot in the post-onset period.

Figure 12 shows the bias score and the equitable threat score (ETS) following Gustafson et al. (2014) over the SCS region in the HR and the LR in the pre- and post-onset period respectively. Both scoring methods define precipitation event as where the precipitation rate exceeds a certain threshold. The bias score represents the ratio of the simulated event amount to the observed event amount, which is used to evaluate the precipitation area. The ETS is a more strictly test that the location of the event is also considered, which is defined as:

where \({n_{fo}}\) is amount of events both forecasted and observed, \({n_{fno}}\) is the number of events forecasted but not observed, \({n_{nfo}}\) is the number of events observed but not forecasted, \({n_f}\) is the number of events forecasted, \({n_o}\) is the number of events observed, and N is the number of total events which could occur.

In the HR, the bias score in the pre-onset period is higher (5) than the post-onset period (3) when the threshold is 0.1 mm h−1, and the bias score in the post-onset period becomes higher compared with the score in the pre-onset period when the threshold increase. The result indicates that the HR overestimates the areas of light precipitation mainly in the pre-onset period, and underestimates the areas of intense precipitation.

The highest ETS in the HR is found in the post-onset period when the threshold is 0.1 mm h−1, the value (0.2) is similar to the result from Gustafson et al. (2014) which evaluated the WRF model using CAM5 physics suite. The result somehow shows that the HR performs well in the post-onset period; however, the ETS in the pre-onset period is only 0.1. It can be seen that the ETS decreases with the increase in threshold in both periods, which shows that heavy precipitation is more difficult to forecast. In summary, the results from the four scoring methods all show that the precipitation bias persists with the increase in horizontal resolution.

Kessler E (1969) On the distribution and continuity of water substance in atmospheric circulations. In: On the distribution and continuity of water substance in atmospheric circulations. American Meteorological Society, Boston, pp 1–84CrossRefGoogle Scholar